Luckily, with massive biomedical data and knowledge being collected and available, along with the advances of deep learning technologies which demonstrated huge success in many application areas, the drug discovery process particularly DTI prediction has been significantly enhanced. Recently, various deep models have shown encouraging performance in DTI predictions. They often take drug and protein data as inputs, cast DTI as a classification problem, and make prediction by feeding the inputs through deep learning models such as deep neural network (DNN) [1]}, deep belief network (DBN) [2]}, and convolutional neural network (CNN) [3]}, [4]}, [5]}. Despite these efforts, the following challenges are still open.
